Abstract

MR imaging can leverage a wide variety of intrinsic contrast mechanisms to provide detailed information regarding the anatomy, function, physiology, and metabolism of biological tissues. However, because of low sensitivity, many experiments that reveal higher-order structure and function have been limited due to inherent trade-offs between data acquisition time, signal-to-noise ratio, and resolution. This paper describes the further development of a statistical framework for MR image reconstruction which helps to mitigate these effects. Specifically, we advocate the collection of high-resolution multi-modal MR imaging data, and utilize the correlation between features in these different images to reduce noise while maintaining resolution. The proposed approach is illustrated with joint reconstruction of relaxometry and spectroscopic imaging data in a mouse model of stroke.

Original language

English (US)

Title of host publication

Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08

Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08. 2008. p. 422-425 4649180 (Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology").

abstract = "MR imaging can leverage a wide variety of intrinsic contrast mechanisms to provide detailed information regarding the anatomy, function, physiology, and metabolism of biological tissues. However, because of low sensitivity, many experiments that reveal higher-order structure and function have been limited due to inherent trade-offs between data acquisition time, signal-to-noise ratio, and resolution. This paper describes the further development of a statistical framework for MR image reconstruction which helps to mitigate these effects. Specifically, we advocate the collection of high-resolution multi-modal MR imaging data, and utilize the correlation between features in these different images to reduce noise while maintaining resolution. The proposed approach is illustrated with joint reconstruction of relaxometry and spectroscopic imaging data in a mouse model of stroke.",

T1 - Further development in anatomically constrained MR image reconstruction

T2 - 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08

AU - Haldar, Justin P.

AU - Wu, Tzy Haw

AU - Wang, Qing

AU - Chen, Chin I.

AU - Song, Sheng Kwei

AU - Liang, Zhi Pei

PY - 2008/12/1

Y1 - 2008/12/1

N2 - MR imaging can leverage a wide variety of intrinsic contrast mechanisms to provide detailed information regarding the anatomy, function, physiology, and metabolism of biological tissues. However, because of low sensitivity, many experiments that reveal higher-order structure and function have been limited due to inherent trade-offs between data acquisition time, signal-to-noise ratio, and resolution. This paper describes the further development of a statistical framework for MR image reconstruction which helps to mitigate these effects. Specifically, we advocate the collection of high-resolution multi-modal MR imaging data, and utilize the correlation between features in these different images to reduce noise while maintaining resolution. The proposed approach is illustrated with joint reconstruction of relaxometry and spectroscopic imaging data in a mouse model of stroke.

AB - MR imaging can leverage a wide variety of intrinsic contrast mechanisms to provide detailed information regarding the anatomy, function, physiology, and metabolism of biological tissues. However, because of low sensitivity, many experiments that reveal higher-order structure and function have been limited due to inherent trade-offs between data acquisition time, signal-to-noise ratio, and resolution. This paper describes the further development of a statistical framework for MR image reconstruction which helps to mitigate these effects. Specifically, we advocate the collection of high-resolution multi-modal MR imaging data, and utilize the correlation between features in these different images to reduce noise while maintaining resolution. The proposed approach is illustrated with joint reconstruction of relaxometry and spectroscopic imaging data in a mouse model of stroke.